Vehicle traffic congestion is an increasingly problematic issue for authorities and road users. Traffic congestion leads to a multitude of downstream effects including significant time wastage on the road, slower speed, increased vehicular queuing and as consequence, increases in traffic delay, financial costs and vehicle carbon emissions. One reason for traffic congestion and delays in reaching a destination is that the driver may not be aware of the correct road layout and the traffic conditions in the lanes ahead. In particular, drivers may not be aware of congestion in each lane, navigation characteristics and navigation routes of other vehicles. Without such information, it may be difficult for a driver to find an optimum lane in order to minimize travel time in a multi-lane road. In the absence of accurate lane information and traffic condition/traffic data in each lane, a driver is likely to remain in the wrong lane or a slow moving lane, which in turn contributes to traffic congestion and delays in reaching the desired destination.
Conventional lane guidance systems suffer from one or more disadvantages in providing a system that efficiently and accurately minimizes travel time for a driver. For example, in determining an optimum lane to minimize travel time, such systems may not consider parameters such as navigation data of vehicles, location, lane, vehicle types, road map data including traffic restrictions at current time, current traffic conditions, weather conditions, surrounding conditions, driver state, road conditions, driver preference, complexity of road segment, familiarity of the road and the switching cycle and switching period of traffic lights. In addition, some of the presently available systems may require manual input about traffic related events, which is cumbersome for a user. Obtaining updated traffic related data about every aspect of road system and traffic events is a significant task. In using such a system, it is difficult to find enough volunteers to constantly update traffic related data in all road segments in order to have a comprehensive up-to-date database of live traffic events. In these systems, redundant or multiple copies of data about the same traffic event may be sent to the server by multiple users, which is inefficient and consumes bandwidth and processing of the user device and the server.
Furthermore, existing systems are limited in their ability to provide comprehensive information to a user. For example, some existing lane guidance systems provide lane guidance only at intersections and may not take useful information into account when determining a lane guidance such as user preference, lane change restrictions, road rules, road and weather conditions.
In some driver guidance systems, the devices communicate directly to a central server. Communicating all the dynamic traffic-related data to and from a central server may be difficult or impossible particularly in a dense road network and heavy traffic conditions. In these systems, the same data may be sent by multiple users, which in turn may waste bandwidth, energy of the device, and consume processing power of a server in filtering out redundant or multiple copies received from multiple users in a congested area. In other existing driver guidance systems, a primary vehicle for which the navigation guidance is determined, the relevant data is received at the primary vehicle directly from sensors in other vehicles and sensors installed in the environment or the like. Transmitting the same data to every vehicle directly in a densely congested area may be inefficient and unnecessary wastage of network resources and device resources.
It will be appreciated that reference herein to “preferred” or “preferably” is intended as exemplary only.